Abstract:
In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.
Abstract:
A patient controls the delivery of therapy through volitional inputs that are detected by a biosignal within the brain. The volitional patient input may be directed towards performing a specific physical or mental activity, such as moving a muscle or performing a mathematical calculation. In one embodiment, a biosignal detection module monitors an electroencephalogram (EEG) signal from within the brain of the patient and determines whether the EEG signal includes the biosignal. In one embodiment, the biosignal detection module analyzes one or more frequency components of the EEG signal. In this manner, the patient may adjust therapy delivery by providing a volitional input that is detected by brain signals, wherein the volitional input may not require the interaction with another device, thereby eliminating the need for an external programmer to adjust therapy delivery. Example therapies include electrical stimulation, drug delivery, and delivery of sensory cues.
Abstract:
A medical device may receive sensor data from sensing sources, and determine confidence levels for sensor data received from each of the plurality of sensing sources. Each of the confidence levels of the sensor data from each of the sensing sources is a measure of accuracy of the sensor data received from respective sensing sources. The medical device may also determine one or more therapy parameter values based on the determined confidence levels, and cause delivery of therapy based on the determined one or more therapy parameter values.
Abstract:
Techniques relate to operating a medical device by classifying a detected posture state of a patient. This classification may be performed by comparing the detected posture state of the patient to posture state definitions available within the system. Each definition may be described in terms of a parameter (e.g., vector) indicative of a direction in three-dimensional space. The posture state definitions may be calibrated by automatically estimating values for these parameters, thereby eliminating the need for the patient to assume each posture state during the calibration process to capture actual parameter values. According to another aspect, the estimated parameter values may be updated as the patient assumes various postures during a daily routine. For instance, estimated vectors initially used to calibrate the posture state definitions may be changed over time to more closely represent posture states the patient actually assumes, and to further adapt to changes in a patient's condition.
Abstract:
Techniques are disclosed for defining a homeostatic window for controlling delivery of electrical stimulation therapy to a patient. In one example, a method includes generating and delivering electrical stimulation therapy to tissue of a patient via electrodes. Further, the method includes adjusting a level of a parameter of the electrical stimulation therapy such that a signal of the patient is not less than a lower bound and not greater than an upper bound. The lower bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce one or more symptoms of a disease while the patient was receiving medication for reduction of the one or more symptoms. Further, the upper bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce the one or more symptoms when the patient was not receiving the medication.
Abstract:
Techniques related to classifying a posture state of a living body are disclosed. One aspect relates to sensing at least one signal indicative of a posture state of a living body. Posture state detection logic classifies the living body as being in a posture state based on the at least one signal, wherein this classification may take into account at least one of posture and activity state of the living body. The posture state detection logic further determines whether the living body is classified in the posture state for at least a predetermined period of time. Response logic is described that initiates a response as a result of the body being classified in the posture state only after the living body has maintained the classified posture state for at least the predetermined period of time. This response may involve a change in therapy, such as neurostimulation therapy, that is delivered to the living body.
Abstract:
Techniques are disclosed to automate determination of therapy parameter values for adaptive deep brain stimulation (aDBS). A medical device may determine differences in power values between a present and a previous power value. Based on the difference being greater than or equal to a threshold value, the medical device may iteratively adjust a present therapy parameter value until the difference in the power values between a present and a previous power value is less than the threshold value.
Abstract:
Techniques are described determining electrodes that are proximate or distal to location of an oscillatory signal source in a patient based on current source densities (CSDs). Processing circuitry may determine, for one or more electrodes of a plurality of electrodes, respective time-varying measurements of CSDs, aggregate, for the one or more electrodes of the plurality electrodes, the respective time-varying measurements of the CSDs to generate respective average level values for the one or more electrodes of the plurality of electrodes, determine, for one or more electrodes of the plurality of electrodes, respective phase-magnitude representations of the time-varying measurements of the CSDs. The respective phase-magnitude representations are indicative of respective magnitudes and phases of a particular frequency component of respective time-varying measurements of the CSDs. The particular frequency component is a frequency component having a largest transform coefficient in a time-varying measurement of a CSD having a largest average level value.
Abstract:
A medical device may receive sensor data from sensing sources, and determine confidence levels for sensor data received from each of the plurality of sensing sources. Each of the confidence levels of the sensor data from each of the sensing sources is a measure of accuracy of the sensor data received from respective sensing sources. The medical device may also determine one or more therapy parameter values based on the determined confidence levels, and cause delivery of therapy based on the determined one or more therapy parameter values.
Abstract:
In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.